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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements an MCP server that accepts reminder requests and schedules them for future execution, then delivers notifications via Slack webhooks or bot integrations. The system uses a scheduling backend (likely cron-based or interval-driven polling) to monitor registered reminders and trigger Slack message delivery at specified times, supporting both one-time and recurring reminder patterns through a standardized MCP protocol interface.
Unique: Exposes reminder scheduling as an MCP server primitive, allowing any MCP-compatible client (including Claude, LLM agents, or custom applications) to trigger reminders without implementing Slack API integration logic directly. This abstracts away webhook management and message formatting into a reusable service.
vs alternatives: Simpler than building custom Slack bot logic for each agent; more flexible than hardcoded reminder systems because it's protocol-agnostic and composable with other MCP tools
Provides parallel reminder delivery capability via Telegram Bot API, allowing reminders to be sent to Telegram users or groups. The implementation integrates with Telegram's bot token authentication and message sending APIs, enabling the same scheduling backend to route notifications to Telegram instead of or in addition to Slack, with support for Telegram-specific message formatting and chat ID targeting.
Unique: Provides Telegram as a first-class notification channel alongside Slack within the same MCP server, allowing developers to abstract away platform-specific bot API differences and route reminders based on user preference or channel configuration without duplicating scheduling logic.
vs alternatives: Offers platform parity with Slack integration in a single server; more maintainable than separate Slack and Telegram reminder services because scheduling logic is unified and only delivery mechanism differs
Implements the Model Context Protocol (MCP) server interface to accept reminder requests from MCP clients (such as Claude, custom LLM agents, or other MCP-compatible applications). The server exposes standardized MCP tools/resources for reminder creation, listing, and cancellation, translating MCP protocol messages into internal scheduling operations and returning structured responses that conform to MCP specification for tool results.
Unique: Exposes reminder functionality as a native MCP server rather than requiring custom tool wrappers or API clients, enabling seamless composition with other MCP tools in agent workflows and allowing Claude to schedule reminders with the same interface it uses for other MCP-based capabilities.
vs alternatives: More composable than REST API wrappers because it integrates directly into MCP agent ecosystems; eliminates need for custom tool definitions or API client code in agent implementations
Supports scheduling reminders using cron expression syntax (e.g., '0 9 * * MON' for 9 AM every Monday), allowing users to define complex recurring patterns without custom logic. The implementation parses cron expressions and converts them into scheduled execution times, leveraging a cron scheduling library or custom parser to determine when reminders should trigger and managing the lifecycle of recurring reminder instances.
Unique: Integrates standard cron expression parsing into the MCP reminder server, allowing agents and developers to express recurring schedules using industry-standard syntax rather than custom scheduling DSLs or imperative scheduling code.
vs alternatives: More expressive than simple 'repeat every N hours' patterns; more portable than custom scheduling logic because cron syntax is universally understood by operations teams
Enables scheduling reminders for a specific point in time (e.g., 'remind me at 2024-01-15 14:30 UTC'), storing the reminder with its target execution time and triggering delivery when the scheduled time arrives. The implementation compares current time against stored reminder timestamps and executes delivery when conditions are met, supporting both ISO 8601 timestamps and Unix epoch formats for maximum compatibility.
Unique: Provides simple absolute timestamp scheduling alongside cron-based recurring reminders, allowing the same server to handle both one-time and recurring use cases without requiring separate services or complex conditional logic.
vs alternatives: Simpler than cron-based scheduling for one-time events; more flexible than hardcoded reminder times because timestamps can be dynamically generated by agents or users
Stores scheduled reminders in a persistent data store (implementation details unclear from available documentation, likely file-based JSON or database), maintaining reminder state across server restarts and allowing queries for active, completed, or cancelled reminders. The system tracks reminder metadata (ID, message, target channel, scheduled time, status) and provides mechanisms to list, update, or cancel reminders before execution.
Unique: unknown — insufficient data on whether persistence uses file-based JSON, embedded database, or external service; implementation details not documented in available sources
vs alternatives: Provides durability guarantees that in-memory-only reminder systems lack; enables reminder management operations (list, cancel, modify) that stateless reminder services cannot support
Allows reminders to be routed to Slack, Telegram, or both simultaneously based on configuration or per-reminder specification, with the server handling platform-specific formatting and delivery logic transparently. The implementation abstracts away platform differences through a unified reminder model and routes each reminder to one or more configured channels, handling failures in one channel without blocking others.
Unique: Unifies Slack and Telegram delivery within a single MCP server, allowing agents to specify 'send reminder to Slack and Telegram' without implementing separate integrations or managing platform-specific logic in agent code.
vs alternatives: More maintainable than separate Slack and Telegram reminder services; more flexible than platform-specific solutions because routing can be configured per reminder or globally
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Reminder at 21/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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